Electrical Sensor Calibration by Fuzzy Clustering with Mandatory Constraint.

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.

Solid Concentration Estimation by Kalman Filter.

One of the major tasks in process industry is solid concentration (SC) estimation in solid-liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation. Consequently, there is complementarity between them, and integrating direct and indirect measurements is helpful to improve the accuracy of SC estimation. In this paper, after recovering the interrelation of these measurements, we proposed a new SC estimation method according to Kalman filter fusion. Focusing on dredging engineering fields, SCs of representative flow pattern were tested. The results show that our proposed methods outperform the fused two types of measurements in real solid-liquid two-phase flow conditions. Additionally, the proposed method has potential to be applied to other fields as well as dredging engineering.

Clustering-Based Component Fraction Estimation in Solid-Liquid Two-Phase Flow in Dredging Engineering.

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid-liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid-liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

An effective measured data preprocessing method in electrical impedance tomography.

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes.

Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

OBJECTIVE: The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients. The commonly-used medical imaging methods have limitations in distinguishing lung nodules to date. A new approach to this problem may be provided by electrical properties of lung nodules. Nevertheless, difference identification is the basis of correct distinction. So, this paper aims to investigate the differences in electrical properties between various lung nodules. METHODS: At variance with existing studies, benign samples were included for analysis. A total of 252 specimens were collected, including 126 normal tissues, 15 benign nodules, 76 adenocarcinomas, and 35 squamous cell carcinomas. The dispersion properties of each tissue were measured over a frequency range of 100 Hz to 100 MHz. And the relaxation mechanism was analyzed by fitting the Cole-Cole plot. The corresponding equivalent circuit was estimated accordingly. RESULTS: Results validated the significant differences between malignant and normal tissue. Significant differences between benign and malignant lesions were observed in conductivity and relative permittivity. Adenocarcinomas and squamous cell carcinomas are significantly different in conductivity, first-order, second-order differences of conductivity, α-band Cole-Cole plot parameters and capacitance of equivalent circuit. The combination of the different features increased the tissue groups' differences measured by Euclidean distance up to 94.7%. CONCLUSION AND SIGNIFICANCE: In conclusion, the four tissue groups reveal dissimilarity in electrical properties. This characteristic potentially lends itself to future diagnosis of non-invasive lung cancer.

Effect of education and muscle relaxation program on anxiety, depression and care burden in caregivers of acute stroke survivors: A randomized, controlled study.

ABSTRACT: This study intended to discover the effect of education and muscle relaxation (EMR) program on anxiety, depression and care burden among caregivers of acute stroke survivors.This randomized, controlled study enrolled a total of 110 caregivers of first-ever acute stroke patients, and randomly assigned to EMR (N = 55) and control (N = 55) groups. The caregivers in the EMR group received 12-month health education and progressive muscle relaxation, and those in control group were provided common rehabilitation advices. Hospital Anxiety and Depression Scale (HADS) and Zarit Caregiver Burden Scale in caregivers were evaluated at the time of patients' discharge from hospital (M0), then at month(M) 3, M6 and M12 after the discharge.HADS-anxiety score, anxiety rate and severity were similar at M0, M3, while were reduced at M6 and M12 in EMR group compared to control group. Furthermore, HADS-depression score was similar at M0 and M3 but was decreased at M6 and M12 in EMR group compared with control group, however, there was no difference of depression rate and severity between the 2 groups at each time point. Moreover, Zarit Caregiver Burden Scale score was similar at M0 and M3, but was decreased at M6 and M12; meanwhile, degree of care burden was similar at M0, M3 and M6, but was reduced at M12 in EMR group compared to control group.EMR program decreases anxiety, depression and care burden in caregivers of acute stroke survivors, suggesting its potential in improving mental health and further promoting quality of lives in these caregivers.

Data preprocessing methods for electrical impedance tomography: a review.

OBJECTIVE: Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e. the 'soft field' effect and the ill-posed problem. In recent years, apart from the development of reconstruction algorithms, some preprocessing methods for measured data or sensitivity maps have also been proposed to reduce these negative effects. It is necessary to find the optimal preprocessing method for various EIT reconstruction algorithms. APPROACH: In this paper, seven typical data preprocessing methods for EIT are reviewed. The image qualities obtained using these methods are evaluated and compared in simulations, and their applicable ranges and combination effects are summarized. MAIN RESULTS: The results show that all the reviewed methods can enhance the quality of EIT reconstructed images to different extents, and there is an optimal one under any given reconstruction algorithm. In addition, most of the reviewed methods do not work well when using the Tikhonov regularization algorithm. SIGNIFICANCE: This paper introduces the preprocessing method to EIT, and the quality of reconstructed images obtained using these methods is evaluated through simulations. The results can provide a reference for practical applications.

The Association Among Medication Beliefs, Perception of Illness and Medication Adherence in Ischemic Stroke Patients: A Cross-Sectional Study in China.

PURPOSE: To examine the association and the mediating effect among medication beliefs, perception of illness, and medication adherence in ischemic stroke patients. PATIENTS AND METHODS: This is a cross-sectional study, 306 ischemic stroke patients recruited from The Second Affiliated Hospital of Harbin Medical University, China between June 2018 and October 2018. The Beliefs about Medications Questionnaire (BMQ) was used to assess a patient's beliefs about medication. The Brief Illness Perceptions Questionnaire (BIPQ) was used to rapidly determine the cognitive and emotional representation of ischemic stroke. Self-reported adherence was assessed using the Medication Adherence Report Scale (MARS). Logistic regression analysis, Pearson correlations, and mediation analysis were used to evaluate the association and mediating effects among medication beliefs, perception of illness, and medication adherence. RESULTS: Overall, 220 (65.48%) participants were non-adherent to their ischemic stroke medications. Non-adherent patients had greater stroke severity (p = 0.031) compared to adherent patients. After adjusting for demographic characteristics, specific concern (odds ratio [OR]: 0.652, 95% confidence interval [CI]: 0.431 to 0.987, p-value [P] = 0.043), and the perception of illness (overall score) (OR: 0.964, 95% CI: 0.944 to 0.985, P = 0.001) were significantly associated with medication adherence in ischemic stroke patients. The mediation analysis showed the significant indirect effects of specific concern, general overuse, and general harm. It suggested that some impacts of medication beliefs have been mediated on medication adherence. CONCLUSION: Perceived concern about adverse effects of medicines and perception of illness have an influential impact on self-reported medication adherence in ischemic stroke patients. To enhance adherence, patients' beliefs about medication and perceptions of their disease should be reconsidered. Future work should investigate interventions to influence patient adherence by addressing concerns about their ischemic stroke medications and the perception of the disease.

Optimal similarity norm for electrical tomography based on Bregman divergence.

Electrical Tomography (ET) is an advanced visualization technique, which can reconstruct all targets in an investigated field based on boundary measurements. Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm, different norms may result in different ET time and spatial resolutions. In the tomographic applications nowadays, Bregman divergence (BD) has attracted increasing attention. BDs are a family of generalized similarity norm, and they can measure the similarity/difference between any two targets more accurately. Specifically, the mostly used similarity norm in the ET process (e.g., L2-norm) is only a special case of the BD family. As the key step of applying BD to the ET process, an execution method is proposed in this paper, together with the selection criteria for the optimal norm in the BD family. Simulations and experiments were conducted, and the results show that the use of an optimal BD can effectively improve the spatial resolution of an ET image.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography.

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented. Instead of linear filters, a singular value decomposition filter is employed as the pre-stage filtering module prior to PSD, which has advantages of zero phase shift, little distortion, and a high signal-to-noise ratio (SNR) in digital signal processing. Simulation and experimental results demonstrated that the proposed method can effectively eliminate the influence of impulsive noise in multi-frequency PSD, and it was capable of achieving a higher SNR and smaller demodulation error.

Amplitude demodulation for electrical capacitance tomography based on singular value decomposition.

Amplitude demodulation is essential in image reconstruction for electrical capacitance tomography (ECT). In this paper, an amplitude demodulation method is proposed based on singular value decomposition (SVD), which can substitute the role of phase-sensitive demodulation in ECT. First, an M × N Hankel matrix is constructed based on a set of discrete samples. Then, SVD operation is performed on the matrix. Finally, the mathematical expression between the sinusoid amplitude and effective singular values is given; i.e., the first two singular values are used to estimate the amplitude information of the acquired signal. The proposed method has the following advantages: (1) since no reference signals are needed, the synchronization between the acquired and reference signals is not necessary; (2) this method can obtain the amplitude information of the acquired signal with a high signal-to-noise ratio (SNR), even in the case of non-integrity period sampling; and (3) SVD itself can also implement the filtering function; thus, no additional low-pass filters are required in the signal conditioning module. The demodulation accuracy and feasibility of the proposed method were verified by numerical simulations and experiments, indicating that it can provide amplitude demodulation with excellent SNR and robust performances.

A general method for evaluating the effects of air pollutants on lung cancer prevalence.

It is widely accepted that some air pollutants are related to lung cancer prevalence. An effective method is proposed to quantitatively evaluate the effects of air pollutants and the interactions between them. The method consisted of three parts: data decomposition, comparable data generation and relationship inference. Firstly, very limited monitoring data published by Geographic Information System were applied to calculate the inhalable air pollution of relatively massive patient samples. Then the investigated area was partitioned into a number of districts, and the comparable data containing air pollutant concentrations and lung cancer prevalence in all districts were generated. Finally, the relationships between pollutants and lung cancer prevalence were concluded by an information fusion tool: Choquet integral. As an example, the proposed method was applied in the investigation of air pollution in Tianjin, China. Overall, SO2, O3 and PM2.5 were the top three factors for lung cancer. And there was obvious positive interaction between O3 and PM2.5 and negative interaction among SO2, O3 and PM10. The effect of SO2 on men was larger than on women. O3 and SO2 were the most important factors for the adenocarcinoma and squamous cell carcinoma, respectively. The effect of SO2 or NO2 on squamous cell carcinoma is obviously larger than that on adenocarcinoma, while the effect of O3 or PM2.5 on adenocarcinoma is obviously larger than that on squamous cell carcinoma. The results provide important suggestions for management of pollutants and improvement of environmental quality. The proposed method without any parameter is general and easily realized, and it sets the foundation for further researches in other cities/countries. Implications: For total lung cancer prevalence, male and female lung cancer prevalence, and adenocarcinoma and squamous cell carcinoma prevalence, the proposed method not only quantify the effect of single pollutant (SO2, NO2, CO, O3, PM2.5, and PM10) but also reveals the correlations between different pollutants such as positive interaction or negative interaction. The proposed method without any geographic predictor and parameter is much easier to realize, and it sets the foundation for further research in other cities/countries. The study results provide important suggestions for the targeted management of different pollutants and the improvement of human lung health.

Optimal distance of multi-plane sensor in three-dimensional electrical impedance tomography.

Electrical impedance tomography (EIT) is a visual imaging technique for obtaining the conductivity and permittivity distributions in the domain of interest. As an advanced technique, EIT has the potential to be a valuable tool for continuously bedside monitoring of pulmonary function. The EIT applications in any three-dimensional (3 D) field are very limited to the 3 D effects, i.e. the distribution of electric field spreads far beyond the electrode plane. The 3 D effects can result in measurement errors and image distortion. An important way to overcome the 3 D effect is to use the multiple groups of sensors. The aim of this paper is to find the best space resolution of EIT image over various electrode planes and select an optimal plane spacing in a 3 D EIT sensor, and provide guidance for 3 D EIT electrodes placement in monitoring lung function. In simulation and experiment, several typical conductivity distribution models, such as one rod (central, midway and edge), two rods and three rods, are set at different plane spacings between the two electrode planes. A Tikhonov regularization algorithm is utilized for reconstructing the images; the relative error and the correlation coefficient are utilized for evaluating the image quality. Based on numerical simulation and experimental results, the image performance at different spacing conditions is evaluated. The results demonstrate that there exists an optimal plane spacing between the two electrode planes for 3 D EIT sensor. And then the selection of the optimal plane spacing between the electrode planes is suggested for the electrodes placement of multi-plane EIT sensor.

Relationships between lung cancer incidences and air pollutants.

BACKGROUND: Statistics on lung cancer incidences and air pollutants show a strong correlation between air pollutant concentrations and pulmonary diseases. And environmental effects on lung cancer incidences remain highly unknown and uncertain in China. OBJECTIVE: This study aims to measure the relationships between different air pollutants and lung cancer incidences in Tianjin. METHODS: One thusand five hundred patients across 27 districts in Tianjin were studied for lung cancer incidences. The patients had come into contact with various air pollutants such as PM2.5, PM10, SO2, NO2, CO, and O3. These pollutants were measured daily and were published via a Geographic Information System across the 27 districts of Tianjin. The air pollutant compositions of environments the patients lived in were determined using the nearest air monitoring station to the patient. And we used rough set theory to measure the relationships between different air pollutants and lung cancer incidences. RESULTS: Different air pollutants and combinations of pollutants impacted lung cancer incidences differently across different districts, sexes, and lung cancer types in Tianjin. CONCLUSIONS: Based on data analysis and interpretation, rough set theory provided data relationships that were objective and interpretable. The method is simple, general, and efficient, and lays the foundation for further applications in other cities.

Optimal three-dimensional reconstruction for lung cancer tissues.

The existing three-dimensional (3D) x-ray reconstruction methods for lung cancer tissue reconstruct the investigated objects based on a series of two-dimensional (2D) image sections and a chosen 3D reconstruction algorithm. However, because these procedures apply the same segmentation method for all 2D image sections, they may not achieve the optimal segmentation for each section. As a result, the reconstructed 3D images have limited spatial resolution. Furthermore, the existing 3D reconstruction method is time-consuming and results in a limited time resolution. This research presents an innovation of 3D reconstruction by reformulating two main components of the method. First, a validity index for fuzzy clustering is used to obtain the optimal segmentations of any 2D x-ray image. The process is realized by automatically determining the optimal number of clusters for the image. Second, unlike the existing 3D reconstruction methods, a fast-FCM algorithm is used to speed up the 2D image segmenting process, thereby raising the time resolution of the 3D reconstruction process. With the aid of commonly used VTK software, the proposed method has been used to visualize four classes of typical lung cancer tissues: adenocarcinoma, large cell carcinoma, small cell carcinoma, and squamous cell carcinoma. Experimental results validate the effectiveness and efficiency of the proposed algorithm. Thus, the method contributes a useful tool for x-ray-based 3D image reconstruction.

A binder-free NiCo2O4 nanosheet/3D elastic N-doped hollow carbon nanotube sponge electrode with high volumetric and gravimetric capacitances for asymmetric supercapacitors.

To increase the volumetric and gravimetric capacitances of supercapacitors, a new class of electrode materials with high electrochemical activity and favorable structures is extremely desired. In this work, a hollow novel nitrogen-doped 3D elastic single-walled carbon nanotube sponge (NSCS) which is ultra lightweight with the lowest density of 0.8 mg cm-3, and has a high open surface structure for electrolyte accessibility and excellent compressible properties as the electrode scaffold has been successfully fabricated by the pyrolysis method which could produce the carbon nanotube sponge easily on a large scale without high-cost and time-consuming processes. Moreover, a NiCo2O4 nanosheet supported on the NSCS has been successfully fabricated. The highest volumetric and gravimetric capacitance of this electrode is 790 F cm-3 at 1.43 g cm-3 and 1618 F g-1 at 0.54 g cm-3 with excellent cycling stability. The density of NiCo2O4/NSCS electrode was adjusted by mechanical compression and the most favorable density of the film for both high volumetric and gravimetric capacitances obtained was 1.21 g cm-3. The thick NiCo2O4/NSCS film of 72 µm has been fabricated at this favorable density, presenting both high volumetric and gravimetric capacitances of 597 F cm-3 and 1074 F g-1 at 1 A g-1, respectively, indicating that the structure of the NSCS is extremely feasible for obtaining a thick film electrode with excellent volumetric and gravimetric capacitances. Furthermore, an asymmetric supercapacitor of NiCo2O4/NSCS//NGN/CNTs was fabricated, exhibiting a high gravimetric energy density of 47.65 W h kg-1 at 536 W kg-1 and a volumetric energy density of 33.44 W h L-1 at 376.16 W L-1.

Using Greedy algorithm: DBSCAN revisited II.

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

Classification of normal and cancerous lung tissues by electrical impendence tomography.

Biological tissue impedance spectroscopy can provide rich physiological and pathological information by measuring the variation of the complex impedance of biological tissues under various frequencies of driven current. Electrical Impedance Tomography (EIT) technique can measure the impedance spectroscopy of biological tissue in medical field. Before application, a key problem must be solved on how to generally distinguish normal tissues from the cancerous in terms of measurable EIT data. In this paper, the impedance spectroscopy characteristics of human lung tissue are studied. On the basis of the measured data of 109 lung cancer patients, Cole-Cole Circle radius (CCCR) and the complex modulus are extracted. In terms of the two characteristics, 71.6% and 66.4% samples of cancerous and normal tissues can be correctly classified, respectively. Furthermore, two characteristics of the measured EIT data of each patient consist of a two-dimensional vector and all such vectors comprise a set of vectors. When classifying the vector set, the rate of correctly partitioning normal and cancerous tissues can be raised to 78.2%. The main factors to affect the classification results on normal and cancerous tissues are generally analyzed. The proposed method will play an important role in further working out an efficient and feasible diagnostic method for potential lung cancer patients, and provide theoretical basis and reference data for electrical impedance tomography technology in monitoring pulmonary function.